Tools

"... Abstract. In well-defined domains such as Physics, Mathematics, and Chemistry, solutions to a posed problem can objectively be classified as correct or incorrect. In ill-defined domains such as medicine, the classification of solutions to a patient problem as correct or incorrect is much more comple ..."

Abstract. In well-defined domains such as Physics, Mathematics, and Chemistry, solutions to a posed problem can objectively be classified as correct or incorrect. In ill-defined domains such as medicine, the classification of solutions to a patient problem as correct or incorrect is much more complex. Typical tutoring systems accept only a small set of approved solutions for each problem scenario fed to the system. Plausible student solutions that fall outside the scope of this small set of approved solutions are rejected as being incorrect, even though these solutions may be acceptable or close to acceptable. This leads to brittleness in the evaluation of student solutions. This paper describes a tutoring system for medical problem-based learning (PBL), which can accept a wide variety of plausible solutions without placing an extensive burden on knowledge acquisition. A widely available medical knowledge source is deployed as a domain ontology, and concept relationships in the ontology are used to make inferences and expand the space of plausible solutions beyond the scope of solutions explicitly provided to the system. Parent-child relationships are used to infer generalized solutions, whereas relationships of synonymy are used to infer alternate solutions. Evaluations of the system after expanding the solution space indicate accuracy close to that of human experts, who agreed among themselves with Pearson Correlation Coefficient of 0.48 and p &lt; 0.05. The system precision dropped by 32%, while the recall increased by five times.

"... Abstract. As digital libraries become more popular, information and knowledge overload has become a pressing required literature searching problem. Problems with searching in digital libraries will worsen as the amount of information/knowledge increases. Traditional digital libraries often index wor ..."

Abstract. As digital libraries become more popular, information and knowledge overload has become a pressing required literature searching problem. Problems with searching in digital libraries will worsen as the amount of information/knowledge increases. Traditional digital libraries often index words and documents while learners think in terms of topics and subjects. As a result, learners cannot determine how well a particular topic and/or subject is covered, or what types of search methods will provide the required information and knowledge without problems. In order to increase the efficiency and quality of the Brita in PuBs project’s activities, an Intelligent Library and Tutoring System for the Brita in PuBs project (ILTS-BP) was developed. ILTS-BP have the ability to personalize, maximize reuse, index, analyze and integrate valuable information and knowledge from a wide selection of existing sources. Also the authors have integrated ILTS-BP with a Voice Stress Analyser Subsystem. ILTS-BP is briefly analyzed in this paper.

"... Nowadays the interest to the adaptive intelligent eLearning systems increases. There are different kind of adaptations one to the content, other to the learning process or to the assessment and so on. The crucial moment for the learner motivation’s is to catch their needs and possibilities and then ..."

Nowadays the interest to the adaptive intelligent eLearning systems increases. There are different kind of adaptations one to the content, other to the learning process or to the assessment and so on. The crucial moment for the learner motivation’s is to catch their needs and possibilities and then to act, i.e. the respond from the system.. The intelligent tutor (system agent(s)) has to decide the most appropriate path through the content based on the collected information for learner as: learning style, learner track through the topics, learner grades and offer further steps. The intelligent agent – tutor keeps all data for every single learner, analyse them and offers next learner’s actions on the system. This paper presents a constructive model based on the learning style of the learners and their ability and how it could be implement in an intelligent eLeaning system. It should be used for self-study in formal and informal education as well as for representing the digitalized cultural and historical heritage for educational purposes.

"... on i te t b se hibits its ability to understand the queries and its inference capabilities. This study proposes to develop a conceptual framework based on archived information. This is true especially for the auroral physicists who depend largely on the shared image infor-tion, Discovery and Integra ..."

on i te t b se hibits its ability to understand the queries and its inference capabilities. This study proposes to develop a conceptual framework based on archived information. This is true especially for the auroral physicists who depend largely on the shared image infor-tion, Discovery and Integration (UDDI) and Web Services Description Language (WSDL) do not provide semantic based on multi-agent systems and ontology technology to assist auroral physicists work with semantically enriched web services and realize the concept of VO. The above mentioned framework can be used for retrieving necessary information, dynamically allow users to define descriptors and operations most appropriate for their purposes, for

"... Abstract. In well-defined domains such as Physics, Mathematics, and Chemistry, solutions to a posed problem can objectively be classified as correct or incorrect. In ill-defined domains such as medicine, the classification of solutions to a patient problem as correct or incorrect is much more comple ..."

Abstract. In well-defined domains such as Physics, Mathematics, and Chemistry, solutions to a posed problem can objectively be classified as correct or incorrect. In ill-defined domains such as medicine, the classification of solutions to a patient problem as correct or incorrect is much more complex. Typical tutoring systems accept only a small set of approved solutions for each problem scenario fed to the system. Plausible student solutions that fall outside the scope of this small set of approved solutions are rejected as being incorrect, even though these solutions may be acceptable or close to acceptable. This leads to brittleness in the evaluation of student solutions. This paper describes a tutoring system for medical problem-based learning (PBL), which can accept a wide variety of plausible solutions without placing an extensive burden on knowledge acquisition. A widely

"... Abstract. In well-defined domains such as Physics, Mathematics, and Chemistry, solutions to a posed problem can objectively be classified as correct or incorrect. In ill-defined domains such as medicine, the classification of solutions to a patient problem as correct or incorrect is much more comple ..."

Abstract. In well-defined domains such as Physics, Mathematics, and Chemistry, solutions to a posed problem can objectively be classified as correct or incorrect. In ill-defined domains such as medicine, the classification of solutions to a patient problem as correct or incorrect is much more complex. Typical tutoring systems accept only a small set of approved solutions for each problem scenario fed to the system. Plausible student solutions that fall outside the scope of this small set of approved solutions are rejected as being incorrect, even though these solutions may be acceptable or close to acceptable. This leads to brittleness in the evaluation of student solutions. This paper describes a tutoring system for medical problem-based learning (PBL), which can accept a wide variety of plausible solutions without placing an extensive burden on knowledge acquisition. A widely available medical knowledge source is deployed as a domain ontology, and concept relationships in the ontology are used to make inferences and expand the space of plausible solutions beyond the scope of solutions explicitly provided to the system. Parent-child relationships are used to infer generalized solutions, whereas relationships of synonymy are used to infer alternate solutions. Evaluations of the system after expanding the solution space indicate accuracy close to that of human experts, who agreed among themselves with Pearson Correlation Coefficient of 0.48 and p &lt; 0.05. The system precision dropped by 32%, while the recall increased by five times.